Name | Craig MacLachlan |
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Affiliation | Met Office |
Research area code | (F7) Science of aquatic & terrestrial environments |
Fellowship Inauguration Year | 2016 |
Short Biography | The Met Office is a world leading centre for weather and climate prediction. We use the same numerical model, the Unified Model, for all timescales from hours to decades. Producing forecasts for all of these timescales requires a vast amount of technical work. There are two key programmes: “Science” and “Technology and Information Services”. The latter group are responsible for the much of the underpinning hardware and IT solutions. The Science section are responsible for the research and development of the scientific software: the Unified Model, ocean models, observations processing, satellite image processing, forecast verification and much more. There are approximately sixty “Scientific Software Engineers” who do underpinning work for these scientific software applications. I lead a team of software engineers and scientists responsible for the monthly to seasonal forecast system at the Met Office. Delivering a robust and reliable system is key to maintaining a world leading seasonal forecast system. Our work covers a wide range of technological areas in the Met Office from research-led model development to operational delivery of products and I am involved in all aspects. The system is comprised of several key Met Office software and hardware systems; we are responsible for writing the software that glues them all together. I work with colleagues from across the Met Office who have different scientific and technological skills. Having expertise across these skills is important. As a senior software engineer I mentor other Met Office software engineers and scientists in software development best practices. I am also responsible for collaborations with other national meteorological services (Republic of Korea, Australia) in seasonal forecasting. By combining the High Performance Computing resource in each of these centres, we will be able to create the largest monthly to seasonal climate prediction system. |
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